Abstract The adage: 'all data is spatial' is especially pertinent in the field of neuroscience, since neuronal data must be indexed by the neuroanatomical location of phenomena or entities under study. Brain atlases are very widely used as laboratory tools, being some of the most highly cited publications in science. This proposal seeks to use brain atlases as a method for indexing data from the literature in a neuroinformatics system. This data includes both textual and graphical information, ranging from highly detailed maps constructed from vector- based spatial primitives, histological photographs and drawings, to textual reports of experimental findings in the literature (which we will analyze on a large scale). These data represent a significant scientific investment that are currently locked away in previously published journal articles (and the detailed data-sets and drawings from researchers that were used to write the articles). A prototype that summarizes the last fifteen years' output of one of the world's most prominent neuroanatomy laboratories is immediately available. This project will develop a collaborative environment to enable neuroscientists to use these valuable maps within the community as a whole by contributing data to a shared system with an open-source system (called 'NeuARt II') that permits querying, overlaying, viewing and annotating such data in an integrated manner. We will also use Natural Language Processing (NLP) techniques to index neuroanatomical references in large numbers of journal articles to be accessible within our infrastructure. As an initial text corpus, we over 110,000 documents taken from last 35 years of published information from the primary neuroanatomical literature. We will construct a neuroanatomical interface that conceptually resembles the 'Google Maps' system, permitting users to use an intuitive spatial interface to browse large amounts of biomedical data in spatial register. The proposed infrastructure will also provide new opportunities to compare and synthesize the anatomical components of neuroscience data multiple modalities (physiological, behavioral, clinical, genetic, molecular, etc.).